Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김회율 | - |
dc.contributor.author | 김대선 | - |
dc.date.accessioned | 2020-03-07T16:30:20Z | - |
dc.date.available | 2020-03-07T16:30:20Z | - |
dc.date.issued | 2013-02 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/133474 | - |
dc.identifier.uri | http://hanyang.dcollection.net/common/orgView/200000421566 | en_US |
dc.description.abstract | In order to solve the problem of overfitting in AdaBoost, we propose a novel AdaBoost algorithm using K-means clustering. AdaBoost is known as an effective method for improving the performance of base classifiers both theoretically and empirically. However, previous studies have shown that AdaBoost is prone to overfitting in overlapped classes. In order to overcome the overfitting problem of AdaBoost, the proposed method uses Kmeans clustering to remove hard-to-learn samples that exist on overlapped region. Since the proposed method does not consider hard-to-learn samples, it suffers less from the overfitting problem compared to conventional AdaBoost. Both synthetic and real world data were tested to confirm the validity of the proposed method. | - |
dc.publisher | 한양대학교 | - |
dc.title | 아다부스트를 위한 훈련 샘플의 재구성 | - |
dc.type | Theses | - |
dc.contributor.googleauthor | 김대선 | - |
dc.sector.campus | S | - |
dc.sector.daehak | 대학원 | - |
dc.sector.department | 전자컴퓨터통신공학과 | - |
dc.description.degree | Master | - |
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